Key points are not available for this paper at this time.
Artificial Intelligence (AI) is gaining momentum in consumer and business applications and many approaches have changed the perspective on AI in Non-Destructive Testing (NDT). Most current discussions about AI in NDT revolve around automated defect detection / recognition (ADR), leaving the enormous potential of AI-based automation in other areas of the inspector's work process unexplored. It is in the nature of NDT that the human inspector's attention should be primarily focused on the parts that show indications or defects. These usually make up only a fraction of the components to be tested, as it is unknown which components actually show defects. This leads to a highly inefficient process that requires an evaluation of all components and thus ties up massive inspection capacities of human experts. The researchers of the German software company sentin have developed a system to process critical findings first and present those to the human inspector. NDT data such as images can be processed as batches leading to a (pre-) sorted dataset considering various indicators such as potential defects, duplicates, missing labels or references and many more. Such a resource-saving, high accuracy Critical Image Detection (CiD) system allows evaluators to find anomalies in complex and extensive datasets fast. After identification further automation can be achieved using a Single Image Detail Analysis system (SiDA), which is more resource intensive, but gives detailed information about the findings, potential defects and their location. The integration of these systems holds significant promises for the future application of AI in NDT. By streamlining the inspection process, they can generate substantial business value through time and cost savings.
Topp et al. (Tue,) studied this question.